1. Field of the Invention
The present invention relates to a method and apparatus For translating words in an artificial neural network, and, in particular, to a method and apparatus for implementing machine translation by associating translated words from original words. In addition, the present invention relates to association of a pattern in an artificial neural network.
2. Description of the Background Art
An artificial neural network comprising a plurality of artificial neurons interconnected each other through links respectively weighted has been recently attempted in order to implement various processes.
For example, an output value of an artificial neuron is determined after receiving output values of the other interconnected artificial neurons according to the Hopfield model which is a general model of the artificial neural network. Moreover, the output value of the artificial neuron is given to the other interconnected artificial neurons for implementing various association processes.
In cases where the number of artificial neurons is N, the output value of an artificial neuron i is O.sub.i, the weight of a link directed from an artificial neuron i to an artificial neuron j is W.sub.ji, an external input value provided to the artificial neuron j from the outside is I.sub.j, a momentum parameter is .delta., and the number of calculation times is t, the output value O.sub.j of the artificial neuron j can be obtained by applying the following equations: EQU O.sub.j.sup.t+1 =f(net.sub.j.sup.t+1) (1) EQU net.sub.j.sup.t+1 =.delta.(.SIGMA.W.sub.ji O.sub.i.sup.t +I.sub.j.sup.t)+(1-.delta.)net.sub.j.sup.t ( 2)
where f is a monotonic increasing function with upper and lower limits.
In this case, if it is desired to have the output of one artificial neuron be similar to the output of another neuron, it is necessary to provide a link with a positive weight between the two artificial neurons. On the other hand, when it is desired to have the output value of one artificial neuron be far from the output value of another neuron, it is necessary to provide a link with a negative weight between the two artificial neurons.
For example, in cases where six artificial neurons 0 to 5 are assigned to picture elements G.sub.0 to G.sub.5 arranged in a hexagonal form in a first conventional example as shown in FIG. 1, the utilization of an artificial neural network as shown in FIG. 2 is required to store six patterns 0 to 5 shown in FIG.8.
The artificial neural network shown in FIG. 2 is composed of the artificial neurons 0 to 5 which each is positioned at a corresponding node and links through which the nodes are interconnected in pairs. Each link is weighted by a weight of 2 or -3.
In the above configuration of the artificial neural network shown in FIG. 2, in cases where the value of the picture element G.sub.1 is 1, the value of the picture element G.sub.5 is 1, and the values of the other picture elements G.sub.0, G.sub.2, G.sub.3, G.sub.4 are not specified, the output values O.sub.0, O.sub.1, O.sub.2, O.sub.3, O.sub.4 and O.sub.5 of the artificial neurons 0 to 5 are initially set as follows EQU O.sub.0 =O.sub.1 =O.sub.2 =O.sub.3 =O.sub.4 =O.sub.5 =0
Moreover, the external inputs I.sub.0, I.sub.1, I.sub.2, I.sub.3, I.sub.4, I.sub.5 are set as follows EQU I.sub.1 =1, I.sub.5 =1, EQU I.sub.0 =I.sub.2 =I.sub.3 =I.sub.4 =0.
As a result, these outputs are finally converged as follows EQU O.sub.0 =O.sub.1 =O.sub.5 =1, EQU O.sub.2 =O.sub.3 =O.sub.4 =-1.
This means that the pattern 0 shown in FIG. 3 is finally associated.
As mentioned above, when information sufficient for associating a desired pattern is given at one time, it is possible to implement the association of the desired pattern in the artificial neural network shown in FIG. 2 according to the Hopfield model.
However, when it is impossible to provide information sufficient for associating the desired pattern to an artificial neural network, it is very difficult to implement the association of the desired pattern in the Hopfield model. Specifically, in cases where partial items of information are fragmentarily provided to the artificial neural network while a pattern to be associated by the provision of the information is changed little by little, the association of the desired pattern is impossible. For example, we will consider the case that the external inputs I.sub.0 to I.sub.5 are given as shown in Table 1:
TABLE 1 __________________________________________________________________________ Times 0 1 2 3 4 5 6 7 8 __________________________________________________________________________ I.sub.0 0 0 0 0 0 0 1 0 1 I.sub.1 0 0 0 0 1 0 0 0 1 I.sub.2 1 0 0 0 0 1 0 1 0 I.sub.3 0 1 0 0 0 0 0 0 0 I.sub.4 0 0 1 0 0 0 0 0 0 I.sub.5 0 0 0 1 0 0 0 0 1 O.sub.0 -1 -1 -1 -1 -1 -1 -1 -1 1 O.sub.1 1 1 -1 -1 -1 -1 -1 -1 1 O.sub.2 1 1 1 -1 -1 1 1 1 -1 O.sub.3 1 1 1 1 1 1 1 1 -1 O.sub.4 -1 -1 1 1 1 1 1 1 -1 O.sub.5 - 1 -1 -1 1 1 -1 -1 -1 1 __________________________________________________________________________
In Table 1, the upper half shows the changes of external inputs I.sub.0 to I.sub.5, and the lower half shows the outputs O.sub.0 to O.sub.5 in finally stable states at the respective times. Moreover, numerals 0 to 8 designated in the horizontal direction show the number of provision times of the external inputs. In this case, it is necessary that the interval in which each of the external inputs I.sub.0 to I.sub.5 is provided to a corresponding artificial neuron be effectively longer than a prescribed time required for stabilizing the artificial neural network. In other words, it is necessary that the artificial network have been already stabilized when the external inputs are given.
In this example, the external inputs are conducted so as to implement both the association of the pattern 3 or 4 by the changes of the external inputs in the time interval from the time 0 to 3 and the association of the pattern 1 or 0 by the changes in the time interval from the time 4 to 8. In other words, partial items of information designating a prescribed pattern to be associated are fragmentarily provided to the artificial neural network at both the interval from the time 0 to 3 and the interval from the time 4 to 8.
Patterns obtained from Table 1 are as follows:
______________________________________ time 0 .fwdarw. Pattern 2, time 4 .fwdarw. Pattern 4, time 1 .fwdarw. Pattern 2, time 5 .fwdarw. Pattern 3, time 2 .fwdarw. Pattern 3, time 6 .fwdarw. Pattern 3, time 3 .fwdarw. Pattern 4, time 7 .fwdarw. Pattern 3, time 8 .fwdarw. Pattern 0 ______________________________________
However, in such a conventional method, as clearly seen from the patterns to be obtained in Table 1, the patterns actually associated in the intervals from the time 4 to 7 differs from the desired pattern 1 or 0, while the desired pattern 3 or 4 can be associated in the intervals from the time 0 to 3. Therefore, it is difficult to follow the changes for associating the desired patterns in turn. The reason is because the external inputs fragmentarily provided are not stored in the artificial neural network in the conventional Hopfield method.
Namely, in such a conventional artificial neural network for associating patterns, when a new pattern which differs from another pattern which has been associated at the present time is received in the artificial neural network, in other words, when the pattern 4 is, for example, associated at the time 3 before the external input is provided to the artificial neuron 1 at the time 4, a partial item of information fragmentarily given by the external input I.sub.1 at the time 4 is ignored as a noise because a small number of external inputs are received in the artificial neural network. On the other hand, if a large number of external inputs are received at one time, the new pattern to be associated is immediately obtained. For example, when the pattern 3 is associated at the time 7 before the external inputs I.sub.0, I.sub.1, I.sub.5 are given at the time 8 to associate an opposite pattern 0, the pattern 3 is exchanged with the pattern 0.
Accordingly, in cases where a series of partial items of information for changing an associated pattern is fragmentarily provided to the artificial neural network, it is very difficult to correctly implement the association because the series of partial items of information are likely to be ignored.
Next, a second conventional example will be described.
For example, four artificial neurons 0,1,2, and 3 are respectively assigned to four picture elements as shown in FIG. 4a. In this case, an artificial neural network as shown in FIG. 4c is utilized to associate one of two patterns as shown in FIG. 4b.
In cases where a picture element assigned to the artificial neuron 0, which is located at the upper right position, has a value 1, and values of the other picture elements are unknown, external inputs are respectively set as follows: EQU I.sub.0 =a positive constant EQU I.sub.1 =I.sub.2 I.sub.3 =0
Therefore, an output value O.sub.0 of the artificial neuron 0 changes to a positive value, and the positive value is propagated to the other artificial neurons. Therefore, all the output values are finally converged as follows: EQU O.sub.0 =O.sub.3 =1 EQU O.sub.1 =O.sub.2 =-1
As a result, the left pattern in FIG. 4b is associated.
As mentioned above, the artificial neural network can be operated quite efficiently in the second conventional example in the case of a small-scale neural network. However, in the case of a large-scale neural network in which a relatively small number of external inputs are provided, the association occasionally becomes impossible.
FIG. 5 shows a part of an artificial neural network according to the second conventional example. The output values of artificial neurons 0 to 5 are assumed to be respectively 1 or -1.
In the network, a group of artificial neurons 1 to 4 has the combination of the output values which each represents a stable state, as follows.
{output value of artificial neuron 1, output value of artificial neuron 2, output value of artificial neuron 3, output value of artificial neuron 4}={1, -1, 1 -1} or {-1, 1, -1, 1} PA1 classifying the original words in the original sentence of the first language as monosemy or polysemy, each of the original words classified as the monosemy linguistically and semantically corresponding to one of the words of the second language, and each of the original words classified as the polysemy linguistically corresponding to a plurality of words of the second language; PA1 assigning the words of the second language to the artificial neurons; PA1 weighting the links through which the artificial neurons assigned the words semantically relevant to one another are interconnected, with positive values, the output values of the artificial neurons interconnected through the links with the positive values being increased in cases where an external input is provided to one of the artificial neurons; PA1 weighting the links through which the artificial neurons which are assigned the words semantically irrelevant to one another are interconnected, with negative values, the output values of the artificial neurons interconnected through the links with the negative values being decreased in cases where an external input is provided to one of the artificial neurons; PA1 providing external inputs Im to artificial neurons Nm which are assigned words Wm of the second language linguistically and semantically corresponding to the original words classified as the monosemy to increase the output values of artificial neurons Nms assigned the words semantically relevant to the words Wm and to decrease the output values of artificial neurons Nmi assigned the words semantically irrelevant to the words Wm; PA1 providing an external input Ip, one after another, to each of the artificial neurons Nms to considerably increase the output values of artificial neurons Nmp which belongs to the artificial neurons Nms and artificial neurons Np assigned the words of the second language linguistically corresponding to the original words classified as the polysemy, the external input Ip provided to each of the artificial neurons Nms being stored therein, and the value of the external input Ip previously stored in the artificial neurons Nms being uniformly reduced to provide again to each of the artificial neurons Nms as past records each time the external input Ip is provided to each of the artificial neurons Nms; PA1 repeatedly converging the output values of all of the artificial neurons each time the external input Ip is provided to each of the artificial neurons Nms; PA1 adopting words Wmp assigned to the artificial neurons Nmp, as translated words, of which the output values are considerably increased; and PA1 adopting the words Wm as the translated words, the translated sentence of the second language being composed of the words Wmp and the words Wm. PA1 sentence structure memory means for storing parts of speech of the original words which are required to analyze the sentence structure of the original sentence; PA1 first language grammar memory means for storing the grammar of a first language of the original sentence; PA1 second language word memory means for storing the words of the second language, wherein each of the original words classified as monosemy linguistically and semantically corresponds to a word of the second language stored in the second language word memory means and each of the original words classified as polysemy linguistically corresponds to a plurality of words of the second language stored in the second language word memory means; PA1 second language grammar memory means for storing the grammar of the second language; PA1 inflection memory means for storing inflection information of the words of the second language; PA1 processing means for PA1 a plurality of artificial neurons are assigned the words of the second language stored in the second language word memory means, PA1 positive links through which the artificial neurons assigned the words semantically relevant to one another are interconnected are weighted with positive values to increase the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, and PA1 negative links through which the artificial neurons assigned the words semantically irrelevant to one another are interconnected are weighted with negative values to decrease the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, PA1 translation means for translating the original sentence into the translated sentence according to a translation process in which PA1 defining an output value O.sub.j of an artificial neuron j regardless of whether or not the artificial neuron j receives an external input value I.sub.j, PA1 classifying the original words in the original sentence of the first language as monosemy or polysemy, each of the original words classified as the monosemy linguistically and semantically corresponding to one of the words of the second language, and each of the original words classified as the polysemy linguistically corresponding to a plurality of words of the second language; PA1 assigning the words of the second language to the artificial neurons; PA1 weighting the links through which the artificial neurons assigned the words semantically relevant to one another are interconnected, with positive values to increase the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons; PA1 weighting the links through which the artificial neurons assigned the words semantically irrelevant to one another are interconnected, with negative values to decrease the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons; PA1 providing external inputs Im to artificial neurons Nm assigned words Wm of the second language linguistically and semantically corresponding to the original words classified as the monosemy to increase the output values of artificial neurons Nms assigned the words semantically relevant to the words Wm and to decrease the output values of artificial neuron Nmi assigned the words semantically irrelevant to the words Wm; PA1 selecting artificial neurons Nh of which the output values are increased by the provision of the external input Im from artificial neurons Np assigned the words of the second language linguistically corresponding to the original words classified as the polysemy; PA1 selecting artificial neurons Nl of which the output values are decreased by the provision of the external input Im from the artificial neurons Np; PA1 providing an external input Iph with a high value to the artificial neurons Nh to considerably increase the output values of the artificial neurons Nh to above a value h(O) according to the equations (1) and (3); PA1 providing an external input Ipl with a low value to the artificial neurons Nl to considerably decrease the output values of the artificial neurons Nl to below a value l(O) according to the equations (1) and (3); PA1 adopting words Wp assigned to the artificial neurons Nh, as translated words, of which the output values are higher than the value h(O); and PA1 adopting the word Wm as the translated words, the translated sentence being composed of the words Wp and the words Wm. PA1 sentence structure memory means for storing parts of speech of the original words which are required to analyze the sentence structure of the original sentence; PA1 first language grammar memory means for storing the grammar of a first language of the original sentence; PA1 second language word memory means for storing the words of the second language, wherein each of the original words classified as monosemy linguistically and semantically corresponds to a word of the second language stored in the second language word memory means and each of the original words classified as polysemy linguistically corresponds to a plurality of words of the second language stored in the second language word memory means; PA1 second language grammar memory means for storing the grammar of the second language; PA1 inflection memory means for storing inflection information of the words of the second language; PA1 processing means for PA1 a plurality of artificial neurons are assigned the words of the second language stored in the second language word memory means, PA1 positive links through which the artificial neurons assigned the words semantically relevant to one another are interconnected are weighted with positive values to increase the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, and PA1 negative links through which the artificial neurons assigned the words semantically irrelevant to one another are interconnected are weighted with negative values to decrease the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, PA1 the output value O.sub.j being varied according to a monotonic increasing function F(net.sub.j) with upper and lower limits h(I.sub.j), l(I.sub.j) as is formulated by equations (1) and (2) EQU O.sub.j =F(net.sub.j), (1) EQU L.ltoreq.l(I.sub.j)&lt;h(I.sub.j).ltoreq.H (2) PA1 wherein the upper limit h(I.sub.j) and the lower limit l(I.sub.j) are monotonic increasing functions, a minimum value of the lower limit l(I.sub.j) equals a low constant L, and a maximum value of the upper limit h(I.sub.j) is a high constant H, and PA1 a value of a variable net.sub.j depending on a value of an external input I.sub.j provided to the artificial neuron j, output values O.sub.i of the other artificial neurons i interconnected with the artificial neuron j through the links weighted with weight parameters W.sub.ji, and the weight parameters W.sub.ji, as is formulated by an equation (3) ##EQU2## wherein the value of the external input I.sub.j equals zero when the artificial neuron J receives no external input, PA1 translation means for translating the original sentence into the translated sentence according to a translation process in which PA1 the sentence structure of the original sentence is analyzed by referring the parts of speech of the original words stored in the sentence structure memory means and the grammar of the first language stored in the original language grammar memory means, PA1 the original words are changed to a series of words which are composed of the words linguistically corresponding to the original words classified as the monosemy and the words Wp, Wm adopted in the processing means, and PA1 a series of words of the second language are changed to the translated sentence by referring the grammar of the second language stored in the translated language grammar memory means and the inflection information of the words of the second language stored in the inflection memory means. PA1 defining an output value O.sub.j of an artificial neuron j regardless of whether or not the artificial neuron j receives an external input value I.sub.j, PA1 classifying the original words in the original sentence of the first language as monosemy or polysemy, each of the original words classified as the monosemy linguistically and semantically corresponding to one of the words of the second language, and each of the original words classified as the polysemy linguistically corresponding to a plurality of words of the second language; PA1 assigning the words of the second language to the artificial neurons, PA1 weighting the links through which the artificial neurons assigned the words semantically relevant to one another are interconnected, with positive values, the output values of the artificial neurons being increased in cases where an external input is provided to one of the artificial neurons; PA1 weighting the links through which the artificial neurons assigned the words semantically irrelevant to one another are interconnected, with negative values, the output values of the artificial neurons being decreased in cases where an external input is provided to one of the artificial neurons; PA1 providing external inputs Im to artificial neurons Nm assigned words Wm of the second language linguistically and semantically corresponding to the original words classified as the monosemy to increase the output values of artificial neurons Nms assigned the words semantically relevant to the words Wm and to decrease the output values of artificial neuron Nmi assigned the words semantically irrelevant to the words Wm; PA1 selecting artificial neurons Nh of which the output values are increased by the provision of the external input Im from artificial neurons Np assigned the words of the second language linguistically corresponding to the original words classified as the polysemy; PA1 providing an external input Ip with a high value, one after another to each of the artificial neurons Nms to considerably increase the output values of the artificial neurons Nms to above a value h(0) according to the equations (1) and (3), the external input Ip provided to each of the artificial neurons Nms being stored therein, and the value of the external input Ip previously stored in the artificial neurons Nms being uniformly reduced to provide again to each of the artificial neurons Nms as past records each time the external input Ip is provided to each of the artificial neurons Nms; PA1 repeatedly converging the output values of all of the artificial neurons each time the external input Ip is provided to each of the artificial neurons Nms; PA1 adopting words Wp assigned to the artificial neurons Nh of which the outputs are higher than the value h(O), as translated words; and PA1 adopting the word Wm as the translated words, the translated sentence being composed of the words Wp and the words Wm. PA1 sentence structure memory means for storing parts of speech of the original words which are required to analyze the sentence structure of the original sentence; PA1 first language grammar memory means for storing the grammar of a first language of the original sentence; PA1 second language word memory means for storing the words of the second language, wherein each of the original words classified as monosemy linguistically and semantically corresponds to a word of the second language stored in the second language word memory means and each of the original words classified as polysemy linguistically corresponds to a plurality of words of the second language stored in the second language word memory means; PA1 second language grammar memory means for storing the grammar of the second language; PA1 inflection memory means for storing inflection information of the words of the second language; PA1 processing means for PA1 a plurality of artificial neurons are assigned the words of the second language stored in the second language word memory means, PA1 positive links through which the artificial neurons assigned the words semantically relevant to one another are interconnected are weighted with positive values to increase the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, and PA1 negative links through which the artificial neurons assigned the words semantically irrelevant to one another are interconnected are weighted with negative values to decrease the output values of the artificial neurons in cases where an external input is provided to one of the artificial neurons, PA1 the output value O.sub.j being varied according to a monotonic increasing function F(net.sub.j) with upper and lower limits h(I.sub.j), l(I.sub.j) as is formulated by equations (1) and (2) EQU O.sub.j =F(net.sub.j), (1) EQU L.ltoreq.l(I.sub.j)&lt;h(I.sub.j).ltoreq.H (2) PA1 wherein the upper limit h(I.sub.j) and the lower limit l(I.sub.j) are monotonic increasing functions, a minimum value of the lower limit l(I.sub.j) equals a low constant L, and a maximum value of the upper limit h(I.sub.j) is a high constant H, and PA1 a value of a variable net.sub.j depending on a value of an external input I.sub.j provided to the artificial neuron j, output values O.sub.i of the other artificial neurons i interconnected with the artificial neuron j through the links weighted by weight parameters W.sub.ji, and the weight parameters W.sub.ji, as is formulated by an equation (3) ##EQU4## wherein the value of the external input I.sub.j equals zero when the artificial neuron j receives no external input, PA1 translation means for translating the original sentence into the translated sentence according to a translation process in which PA1 the sentence structure of the original sentence is analyzed by referring the parts of speech of the original words stored in the sentence structure memory means and the grammar of the first language stored in the original language grammar memory means, PA1 the original words are changed to a series of words which are composed of the words linguistically corresponding to the original words classified as the monosemy and the words Wp, Wm adopted in the processing means, and PA1 a series of words of the second language are changed to the translated sentence by referring the grammar of the second language stored in the translated language grammar memory means and the inflection information of the words of the second language stored in the inflection memory means. PA1 selecting a group of artificial neurons Ns of which the arrangement indicates a pattern to be associated by the artificial neural network; PA1 providing an external input Ip, one after another, to each of the artificial neurons Ns to increase the output values of the artificial neurons Ns, the external input Ip provided to each of the artificial neurons Ns being stored therein, and the value of the external input Ip previously stored in the artificial neurons Ns being uniformly reduced to provide again to each of the artificial neurons Ns as past records each time the external input Ip is provided to each of the artificial neurons Ns; PA1 repeatedly converging the output values of all the artificial neurons each time the external input Ip is provided to each of the artificial neurons Ns; PA1 associating the pattern with the arrangement of the artificial neurons Ns of which the output values are high. PA1 defining an output value O.sub.j of an artificial neuron j receiving an external input I.sub.j, PA1 defining an output value O.sub.j of an artificial neuron j receiving no external input I.sub.j, PA1 associating the pattern B with the arrangement of the artificial neurons with the high output value. PA1 defining an output value O.sub.j of an artificial neuron j regardless of whether or not the artificial neuron j receives an external input I.sub.j, PA1 providing an external input I.sub.k with a high value to an artificial neuron k and providing an external input value I.sub.p with a low value to an artificial neuron p to converge the output values of the artificial neurons according to the equations (1) and (3), PA1 associating the pattern B with the arrangement of the artificial neurons with the high output values. PA1 defining an output value O.sub.j off an artificial neuron j receiving an external input I.sub.j, PA1 defining an output value O.sub.j of an artificial neuron j receiving no external input I.sub.j, PA1 providing an external input Ip with a high value, one after another, to each of the artificial neurons Ns to increase the output values of the artificial neurons Ns to above the high limit h, the external input Ip provided to each of the artificial neurons Ns being stored therein, and the value of the external input Ip previously stored in the artificial neurons Ns being uniformly reduced to provide again to each of the artificial neurons Ns as past records each time the external input Ip is provided to each of the artificial neurons Ns; PA1 repeatedly converging the output values of all the is artificial neurons according to the equations (1) to (4) each time the external input Ip is provided to each of the artificial neurons Ns; and PA1 associating the pattern B with the arrangement of the artificial neurons Ns of which the output values are high. PA1 defining an output value O.sub.j of an artificial neuron j regardless of whether or not the artificial neuron j receives an external input I.sub.j, PA1 selecting a group of artificial neurons Ns of which the arrangement indicates a pattern B differing from the pattern A; PA1 providing an external input Ip with a high value, one after another, to each of the artificial neurons Ns to increase the output values of the artificial neurons Ns to above the high limit h, the external input Ip provided to each of the artificial neurons Ns being stored therein, and the value of the external input Ip previously stored in the artificial neurons Ns being uniformly reduced to provide again to each of the artificial neurons Ns as past records each time the external input Ip is provided to each of the artificial neurons Ns; PA1 repeatedly converging the output values of all the artificial neurons according to the equations (1) to (4) each time the external input Ip is provided to each of the artificial neurons Ns; and PA1 associating the pattern B with the arrangement of the artificial neurons Ns of which the output values are high.
In the network, one of the suitable stable states should be selected not by the output of the artificial neuron 0 but by that of the artificial neuron 5.
The reason is that the absolute value of the weight provided to a link interconnecting between the artificial neuron 5 and the artificial neuron 4 is far higher than that of the weight provided to a link interconnecting between the artificial neuron 0 and the artificial neuron 1.
However, because of the input time delay resulting from the difference between external inputs or propagation delay, it can be assumed that the propagation time from the artificial neuron 0 to the artificial neuron 1 is faster than that from the artificial neuron 5 to the artificial neuron 4.
If such a propagation difference is sufficiently large, it can also be assumed that a partial item of information is provided to the artificial neurons 2,3,4 from the artificial neuron 1 receiving the output value of the artificial neuron 0, so that the artificial neurons 2, 3, 4 are activated by the information from the artificial neuron 1 and reach an incorrect stable state.
Once, these artificial neurons are stabilized, they cannot be shifted to a correct stable state regardless of whether a high absolute output value is provided by the artificial neuron 5.
As is described above, in case of the first conventional example for associating patterns in an artificial neural network, because the external inputs fragmentarily provided are not stored, the association cannot be correctly implemented when a series of partial items of information designating a specific pattern is fragmentarily provided.
On the other hand, in the second conventional example for associating patterns in an artificial neural network, because artificial neurons receiving external inputs provide output values in the same range as others not receiving external inputs, it is sometimes impossible to correctly implement the association.
In the second conventional example, each of the artificial neurons receiving the external inputs should provide an output value with the same sign (positive or negative) as the external inputs. Therefore, the output value is expected to form a portion of a desired stable state. On the other hand, each of the artificial neurons not receiving external inputs is required to provide an output value which can lead the artificial neurons to a more stable state.
Accordingly, it is preferred that the external inputs be regarded to be important in the artificial neurons receiving them. Moreover, it is preferable that the output values of such artificial neurons have more important meaning than the artificial neurons not receiving external inputs.
However, when the output value of each artificial neuron not receiving external inputs reaches a high absolute value due to noise or output values from other artificial neurons, even if an attempt is made to change the output values of the artificial neurons receiving external inputs thereafter, it is impossible to make the alteration.
Accordingly, in the First and the second conventional examples, when a series of partial items of information are fragmentarily provided to the artificial neurons, it is very difficult to implement the association. Of course, it is preferable to implement the association in a short time, but when the association is completed by utilizing only the small number of items of information provided at first to implement the association in a short time, a user often obtains an undesired pattern. Moreover, once an undesired pattern is associated, it is very difficult to revise it.
In addition, in cases where the association implemented by the artificial neural network is utilized for machine translation of original words, translated words are not correctly obtained if the artificial neural network easily associates undesired words. The reason is that each of the original words classified as polysemy linguistically corresponds to a plurality of the translated words, while each of the original words classified as monosemy linguistically corresponds to a translated word.
Therefore, when an original sentence written by the original words are translated into a translated sentence, a translated word semantically corresponding to an original word classified as the polysemy must be correctly selected from words linguistically corresponding to the original word while considering a topic of the original sentence.